Learning Objectives
The term R is used to refer to both the programming language and the software that interprets the scrips written using it.
RStudio is currently a popular way to write R scrips and also interact with the R software.
With R, the results of your analysis do not depend on remembering a succession of pointing and clicking, but instead on a series of written commands. That means that if (when!) you want to redo your analysis because you collected more data, or you want to run the same analyses on a different dataset, you don’t have to remember which button your clicked to obtain your results, you just have to run the script again.
Working with scripts makes the steps you used in your analysis clear, and the code you write can be inspected by someone else for their own work, or to give you feedback.
Working with scripts forces you to have a deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.
Reproducibility is when someone else (including your future self) can obtain the same results from the same dataset when using the same analysis.
Short-term goals:
* Are the tables and figures reproducible from the code and data?
* Does the code actually do what you think it does?
* In addition to what was done, is it clear why is was done? (e.g., how were parameter settings chosen?)
Long-term goals:
* Can the code be used for other data?
* Can you extend the code to do other things?
An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with those requirements.
With 10,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine statistical approaches from many scientific disciplines to best suit the analytical framework you need to analyze your data. For instance, R has packages for image analysis, GIS, time series, population genetics, bioinformatics and a lot more.
(more about packages soon)
The skills you learn with R scale easily with the size of your dataset. Whether your dataset has hundreds or millions of lines, it won’t make much difference to you.
R is designed for data analysis. It comes with special data structures and data types that make handling of missing data and statistical factors convenient.
R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.
The plotting functionalities in R are endless, and allow you to adjust any aspect of your graph to convey most effectively the message from your data.
Thousands of people use R daily. Many of them are willing to help you through mailing lists and websites such as Stack Overflow, or on the RStudio community.
We will use RStudio IDE to write code, navigate the files on our computer, inspect the variables we are going to create, and visualize the plots we will generate. RStudio can also be used for other things (e.g., version control, developing packages, writing Shiny apps) that we will not cover during the workshop.
RStudio is divided into 4 “Panes”: the Source for your scripts and documents (top-left, in the default layout), your Environment/History (top-right), your Files/Plots/Packages/Help/Viewer (bottom-right), and the R Console (bottom-left). The placement of these panes and their content can be customized (see menu, Tools -> Global Options -> Pane Layout).
One of the advantages of using RStudio is that all the information you need to write code is available in a single window. Additionally, with many shortcuts, auto completion, and highlighting for the major file types you use while developing in R, RStudio will make typing easier and less error-prone.
It is good practice to keep a set of related data, analyses, and text self-contained in a single folder, called the working directory. All of the scripts within this folder can then use relative paths to files that indicate where inside the project a file is located (as opposed to absolute paths, which point to where a file is on a specific computer). Working this way makes it a lot easier to move your project around on your computer and share it with others without worrying about whether or not the underlying scripts will still work.
RStudio provides a helpful set of tools to do this through its “Projects” interface, which not only creates a working directory for you, but also remembers its location (allowing you to quickly navigate to it) and optionally preserves custom settings and open files to make it easier to resume work after a break. Go through the steps for creating an “R Project” for this tutorial below.
RStudio’s default preferences generally work well, but saving a workspace to .RData can be cumbersome, especially if you are working with larger datasets. To turn that off, go to Tools –> ‘Global Options’ and select the ‘Never’ option for ‘Save workspace to .RData’ on exit.’
Using a consistent folder structure across your projects will help keep things organized, and will also make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, you may create directories (folders) for scripts, data, and documents.
data_in/ Use this folder to store your raw data and intermediate datasets you may create for the need of a particular analysis. For the sake of transparency and provenance, you should always keep a copy of your raw data accessible and do as much of your data cleanup and preprocessing programmatically (i.e., with scripts, rather than manually) as possible. Separating raw data from processed data is also a good idea.*_out/ This is where to put the processed data or figures that aren’t in manuscript form.manuscript/ This is the folder that contains each table and figure that is used in the manuscriptother/ This is the place to keep outlines, drafts, and other text or files that aren’t used in your scripts.scripts/ This is the location to keep your R scripts for different analyses or plotting, and potentially a separate folder for your functions (more on that later).You may want additional directories or subdirectories depending on your project needs, but these should form the backbone of your working directory. Here’s an example of one of mine for a recent manuscript:
For this workshop, we will need a data_in/ folder to store our raw data, and we will use data_out/ for when we learn how to export data as CSV files, scripts to save the script folders we’ll write and figures_out/ folder for the figures that we will save.
Under the Files tab on the right of the screen, click on New Folder and create a folder named data_in within your newly created working directory (e.g., ~/RStats/data_in). (Alternatively, type dir.create("data_in") at your R console.) Repeat these operations to create a data_out, scripts and a figures_out folders.
We are going to keep the script in the root of our working directory because we are only going to use one file and it will make things easier.
Your working directory should now look like this:
The working directory is an important concept to understand. It is the place from where R will be looking for and saving the files. When you write code for your project, it should refer to files in relation to the root of your working directory and only need files within this structure.
Using RStudio projects makes this easy and ensures that your working directory is set properly. If you need to check it, you can use getwd(). If for some reason your working directory is not what it should be, you can change it in the RStudio interface by navigating in the file browser where your working directory should be, and clicking on the blue gear icon “More”, and select “Set As Working Directory”. Alternatively you can use setwd("/path/to/working/directory") to reset your working directory. However, your scripts should not include this line because it will fail on someone else’s computer.
The basis of programming is that we write down instructions for the computer to follow, and then we tell the computer to follow those instructions. We write, or code, instructions in R because it is a common language that both the computer and we can understand. We call the instructions commands and we tell the computer to follow the instructions by executing (also called running) those commands.
There are two main ways of interacting with R: by using the console or by using script files (plain text files that contain your code). The console pane (in RStudio, the bottom left panel) is the place where commands written in the R language can be typed and executed immediately by the computer. It is also where the results will be shown for commands that have been executed. You can type commands directly into the console and press Enter to execute those commands, but they will be forgotten when you close the session.
Because we want our code and workflow to be reproducible, it is better to type the commands we want in the script editor, and save the script. This way, there is a complete record of what we did, and anyone (including our future selves!) can easily replicate the results on their computer.
RStudio allows you to execute commands directly from the script editor by using the Ctrl + Enter shortcut (on Macs, Cmd + Return will work, too). The command on the current line in the script (indicated by the cursor) or all of the commands in the currently selected text will be sent to the console and executed when you press Ctrl + Enter. You can find other keyboard shortcuts in this “RStudio cheatsheet about the RStudio IDE”.
At some point in your analysis you may want to check the content of a variable or the structure of an object, without necessarily keeping a record of it in your script. You can type these commands and execute them directly in the console. RStudio provides the Ctrl + 1 and Ctrl + 2 shortcuts allow you to jump between the script and the console panes.
If R is ready to accept commands, the R console shows a > prompt. If it receives a command (by typing, copy-pasting or sent from the script editor using Ctrl + Enter), R will try to execute it, and when ready, will show the results and come back with a new > prompt to wait for new commands.
If R is still waiting for you to enter more data because it isn’t complete yet, the console will show a + prompt. It means that you haven’t finished entering a complete command. This is because you have not ‘closed’ a parenthesis or quotation, i.e. you don’t have the same number of left-parentheses as right-parentheses, or the same number of opening and closing quotation marks. When this happens, and you thought you finished typing your command, click inside the console window and press Esc; this will cancel the incomplete command and return you to the> prompt.
The material we cover during this workshop will give you an initial taste of how you can use R to analyze data for your own research. However, you will need to learn more to do advanced operations. The best way to become proficient and efficient at R, as with any other tool, is to use it to address your actual research questions. As a beginner, it can feel daunting to have to write a script from scratch, and given that many people make their code available online, modifying existing code to suit your purpose might make it easier for you to get started.
The following part of this section is from “Modern Dive Section 2.2.3”
Learning to code/program is very much like learning a foreign language, it can be very daunting and frustrating at first. Such frustrations are very common and it is very normal to feel discouraged as you learn. However just as with learning a foreign language, if you put in the effort and are not afraid to make mistakes, anybody can learn.
Here are a few useful tips to keep in mind as you learn to program:
how real programmers troubleshoot
One of the fastest ways to get help, is to use the RStudio help interface. This panel by default can be found at the lower right hand panel of RStudio. As seen in the screenshot, by typing the word “Mean”, RStudio tries to also give a number of suggestions that you might be interested in. The description is then shown in the display window.
I know the name of the function I want to use, but I’m not sure how to use it
If you need help with a specific function, let’s say barplot(), you can type:
?barplot
If you just need to remind yourself of the names of the arguments, you can use:
args(lm)
## function (formula, data, subset, weights, na.action, method = "qr",
## model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
## contrasts = NULL, offset, ...)
## NULL
I want to use a function that does X, there must be a function for it but I don’t know which one… If you are looking for a function to do a particular task, you can use the help.search() function, which is called by the double question mark ??. However, this only looks through the installed packages for help pages with a match to your search request
??kruskal
If you can’t find what you are looking for, you can use the “rdocumentation.org” website that searches through the help files across all packages available.
Finally, a generic Google or internet search “R” will often either send you to the appropriate package documentation or a helpful forum where someone else has already asked your question.
Start by googling the error message. However, this doesn’t always work very well because often, package developers rely on the error catching provided by R. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. “subscript out of bounds”). If the message is very generic, you might also include the name of the function or package you’re using in your query.
However, you should check Stack Overflow. Search using the [r] tag. Most questions have already been answered, but the challenge is to use the right words in the search to find the answers:
The key to receiving help from someone is for them to rapidly grasp your problem. You should make it as easy as possible to pinpoint where the issue might be.
Try to use the correct words to describe your problem. For instance, a package is not the same thing as a library. Most people will understand what you meant, but others have really strong feelings about the difference in meaning. The key point is that it can make things confusing for people trying to help you. Be as precise as possible when describing your problem.
If possible, try to reduce what doesn’t work to a simple reproducible example. If you can reproduce the problem using a very small data frame instead of your 50,000 rows and 10,000 columns one, provide the small one with the description of your problem. When appropriate, try to generalize what you are doing so even people who are not in your field can understand the question. For instance instead of using a subset of your real dataset, create a small (3 columns, 5 rows) generic one. For more information on how to write a reproducible example see “this article by Hadley Wickham”.
Based on largely on material from The Carpentries made available under the Creative Commons Attribution license.